import tensorflow as tf
from PIL import Image, ImageFilter
import matplotlib.pyplot as plt
import time

def imageprepare(): 
    im = Image.open('/home/lfz_5/Files/robot/Projects/train/test/9-2.png') 

    im = im.convert('L')
    tv = list(im.getdata()) 
    tva = [(255-x)*1.0/255.0 for x in tv] 
    return tva

result=imageprepare()
x=tf.placeholder("float",[None,784])
y_=tf.placeholder("float",[None,10])

def weight_variable(shape):
    initial=tf.truncated_normal(shape,stddev=0.1)
    return tf.Variable(initial)
def bias_variable(shape):
    initial=tf.truncated_normal(shape=shape,stddev=0.1)
    return tf.Variable(initial)

def conv2d(x,W):
    return tf.nn.conv2d(x,W,strides=[1,1,1,1],padding='SAME')
def max_pool_2x2(x):
    return tf.nn.max_pool(x,ksize=[1,2,2,1],strides=[1,2,2,1],padding='SAME')

W_conv1=weight_variable([5, 5, 1, 32])
b_conv1=bias_variable([32])
x_image=tf.reshape(x, [-1,28,28,1])
h_conv1=tf.nn.relu(conv2d(x_image, W_conv1) + b_conv1)
h_pool1=max_pool_2x2(h_conv1)

W_conv2=weight_variable([5, 5, 32, 64])
b_conv2=bias_variable([64])
h_conv2=tf.nn.relu(conv2d(h_pool1, W_conv2) + b_conv2)
h_pool2=max_pool_2x2(h_conv2)

W_fc1 = weight_variable([7 * 7 * 64, 1024])
b_fc1 = bias_variable([1024])

h_pool2_flat = tf.reshape(h_pool2,[-1,7*7*64])
h_fc1 = tf.nn.relu(tf.matmul(h_pool2_flat, W_fc1)+b_fc1)
keep_prob = tf.placeholder("float")
h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob)

W_fc2=weight_variable([1024, 10])
b_fc2=bias_variable([10])

y_conv=tf.nn.softmax(tf.matmul(h_fc1_drop, W_fc2) + b_fc2)

cross_entropy=-tf.reduce_sum(y_*tf.log(y_conv))
train_step=tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)
correct_prediction=tf.equal(tf.argmax(y_conv,1), tf.argmax(y_,1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float"))
saver=tf.train.Saver()
with tf.Session() as sess:
    sess.run(tf.global_variables_initializer())
    saver.restore(sess, "/home/lfz_5/Files/robot/Projects/train/model.ckpt") 
    start_time=time.time()
    prediction=tf.argmax(y_conv,1)
    predint=prediction.eval(feed_dict={x: [result],keep_prob: 1.0}, session=sess)
    du=time.time()-start_time
    print('number:%d'%predint[0])
    print('time:%.3f'%du)
    a=y_conv.eval(feed_dict={x: [result],keep_prob: 1.0}, session=sess)
    print('accuracy:%.2f%%'%(a[0,predint[0]]*100))
    